Adversarial Laser Spot: Robust and Covert Physical-World Attack to DNNs

TL;DR

This paper introduces AdvLS, a novel light-based physical attack using laser spots that is robust, covert, and effective against DNNs, with potential security implications for vision systems.

Contribution

AdvLS is the first daytime light-based physical attack on DNNs optimized via genetic algorithms for robustness and covertness.

Findings

AdvLS achieves high robustness in digital and physical environments.

AdvLS demonstrates excellent covertness during daytime attacks.

Adversarial perturbations from AdvLS show strong transferability across models.

Abstract

Most existing deep neural networks (DNNs) are easily disturbed by slight noise. However, there are few researches on physical attacks by deploying lighting equipment. The light-based physical attacks has excellent covertness, which brings great security risks to many vision-based applications (such as self-driving). Therefore, we propose a light-based physical attack, called adversarial laser spot (AdvLS), which optimizes the physical parameters of laser spots through genetic algorithm to perform physical attacks. It realizes robust and covert physical attack by using low-cost laser equipment. As far as we know, AdvLS is the first light-based physical attack that perform physical attacks in the daytime. A large number of experiments in the digital and physical environments show that AdvLS has excellent robustness and covertness. In addition, through in-depth analysis of the experimental data, we find that the adversarial perturbations generated by AdvLS have superior adversarial attack migration. The experimental results show that AdvLS impose serious interference to advanced DNNs, we call for the attention of the proposed AdvLS. The code of AdvLS is available at: https://github.com/ChengYinHu/AdvLS